CN111033418B

CN111033418B - Automatic speed control command calibration system for an autonomous vehicle

Info

Publication number: CN111033418B
Application number: CN201880004917.5A
Authority: CN
Inventors: 朱帆; 孔旗; 马霖; 江汇; 陶佳鸣; 张亮亮
Original assignee: Baidu com Times Technology Beijing Co Ltd; Baidu USA LLC
Current assignee: Baidu com Times Technology Beijing Co Ltd; Baidu USA LLC
Priority date: 2018-07-09
Filing date: 2018-07-09
Publication date: 2023-08-08
Anticipated expiration: 2038-07-09
Also published as: EP3625635A4; US20210179101A1; US11214251B2; CN111033418A; JP2020529348A; JP6952117B2; EP3625635B1; EP3625635A1; WO2020010489A1

Abstract

A speed control command auto calibration system for an autonomous vehicle receives a first control command and a speed measurement of an ADV (901). The system determines an expected acceleration of the ADV based on the speed measurement and the first control command (902). The system receives an acceleration measurement of the ADV (903). The system determines a feedback error based on the acceleration measurement and the expected acceleration (904). The system updates a portion of the calibration table based on the determined feedback error (905). The system generates a second control command to control the ADV based on the calibration table with the updated portion to autonomously control the ADV according to the second control command (906).

Description

Automatic speed control command calibration system for an autonomous vehicle

Technical Field

Embodiments of the present disclosure relate generally to operating an autonomous vehicle. More particularly, embodiments of the present disclosure relate to a speed control command auto calibration system for an autonomous vehicle (ADV).

Background

A vehicle operating in an autonomous mode (e.g., unmanned) may free up occupants, particularly the driver, from some driving-related responsibilities. When operating in the autonomous mode, the vehicle may navigate to various locations using on-board sensors, allowing the vehicle to travel with minimal human interaction or without some of any passengers.

Motion planning and control are key operations in ADV. However, conventional motion planning operations primarily plan a given path based on its curvature and speed, regardless of differences in characteristics of different types of vehicles. The same motion planning and control applied to different types of vehicles or to the same type of vehicle but with different loads may differ in terms of actual rate output.

The vehicle speed is a key input to the control module of the ADV, and the speed of the ADV may be different because (1) the vehicle is different and (2) the load of the vehicle is different. There is a lack of effective methods of calibrating vehicle speed.

Disclosure of Invention

In a first aspect, the present disclosure provides a computer-implemented method for controlling an autonomous vehicle (ADV), the method comprising: receiving a first control command and a speed measurement of the ADV; determining an expected acceleration of the ADV based on the speed measurement and the first control command; receiving an acceleration measurement of the ADV; determining a feedback error based on the acceleration measurement and the expected acceleration; updating a portion of the calibration table based on the determined feedback error; and generating a second control command to control the ADV based on the calibration table with the updated portion to autonomously control the ADV in accordance with the second control command.

In a second aspect, the present disclosure provides a non-transitory machine-readable medium having stored thereon instructions that, when executed by a processor, cause the processor to perform operations comprising: receiving a first control command and a speed measurement of the ADV; determining an expected acceleration of the ADV based on the speed measurement and the first control command; receiving an acceleration measurement of the ADV; determining a feedback error based on the acceleration measurement and the expected acceleration; updating a portion of the calibration table based on the determined feedback error; and generating a second control command to control the ADV based on the calibration table with the updated portion to autonomously control the ADV in accordance with the second control command.

In a third aspect, the present disclosure provides a data processing system comprising a processor and a memory, wherein the memory is coupled to the processor to store instructions that, when executed by the processor, cause the processor to perform operations comprising: receiving a first control command and a speed measurement of the ADV; determining an expected acceleration of the ADV based on the speed measurement and the first control command; receiving an acceleration measurement of the ADV; determining a feedback error based on the acceleration measurement and the expected acceleration; updating a portion of the calibration table based on the determined feedback error; and generating a second control command to control the ADV based on the calibration table with the updated portion to autonomously control the ADV in accordance with the second control command.

Drawings

Embodiments of the present disclosure are illustrated by way of example, and not by way of limitation, in the figures of the accompanying drawings in which like reference numerals refer to similar elements.

FIG. 1 is a block diagram illustrating a networked system according to one embodiment.

FIG. 2 is a block diagram illustrating an example of a sensor and control system utilized by an autonomous vehicle according to one embodiment.

Fig. 3A-3B are block diagrams illustrating examples of a perception and planning system used by an autonomous vehicle according to some embodiments.

FIG. 4 is a block diagram illustrating an example of an auto-calibration module according to one embodiment.

FIG. 5 is an example of a calibration table according to one embodiment.

FIG. 6 is a block diagram illustrating an exemplary feedback error limiter according to one embodiment.

Fig. 7 is a block diagram illustrating an example of a portion of a calibration table according to one embodiment.

Fig. 8A-8D are block diagrams illustrating examples of automatic calibration updates according to one embodiment.

FIG. 9 is a flow chart illustrating a method according to one embodiment.

FIG. 10 is a flow chart illustrating a method according to one embodiment.

FIG. 11 is a block diagram illustrating an example of a data processing system according to one embodiment.

Detailed Description

Various embodiments and aspects of the disclosure will be described with reference to details discussed below, which are illustrated in the accompanying drawings. The following description and drawings are illustrative of the disclosure and are not to be construed as limiting the disclosure. Numerous specific details are described to provide a thorough understanding of various embodiments of the present disclosure. However, in certain instances, well-known or conventional details are not described in order to provide a concise discussion of embodiments of the present disclosure.

Reference in the specification to "one embodiment" or "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the disclosure. The appearances of the phrase "in one embodiment" in various places in the specification are not necessarily all referring to the same embodiment.

Speed control allows the control module of the ADV to follow the planned trajectory accurately. Typically, the speed control is calibrated (e.g., using a calibration matrix and/or a calibration table) to adjust the output acceleration and/or rate according to the instantaneous load of the vehicle model and/or ADV. The calibrated output may prevent the ADV from exceeding or falling below the planned speed of the vehicle between planning periods. Calibration tables are typically manually calibrated when the ADV is offline. However, manual calibration can be cumbersome because each vehicle must be calibrated individually. Furthermore, as vehicle performance decreases over time, recalibration is also necessary. Furthermore, for the same vehicle carrying different weights/loads, the fixed table may no longer be accurate. Thus, based on control and sensor inputs, the calibration table can be automatically updated in real time to save time and effort.

According to one aspect, a system receives a first control command and a speed measurement of an ADV. The system determines an expected acceleration of the ADV based on the speed measurement and the first control command. The system receives an acceleration measurement of the ADV. The system determines a feedback error based on the acceleration measurement and the expected acceleration. The system updates a portion of the calibration table based on the determined feedback error. The system generates a second control command to control the ADV based on the calibration table with the updated portion to autonomously control the ADV in accordance with the second control command.

Fig. 1 is a block diagram illustrating an autonomous vehicle network configuration according to one embodiment of the present disclosure. Referring to fig. 1, a network configuration 100 includes an autonomous vehicle 101 communicatively coupleable to one or more servers 103-104 via a network 102. Although one autonomous vehicle is shown, multiple autonomous vehicles may be coupled to each other and/or to servers 103-104 through network 102. The network 102 may be any type of network, for example, a wired or wireless Local Area Network (LAN), a Wide Area Network (WAN) such as the Internet, a cellular network, a satellite network, or a combination thereof. Servers 103-104 may be any type of server or cluster of servers, such as a network or cloud server, an application server, a backend server, or a combination thereof. The servers 103 to 104 may be data analysis servers, content servers, traffic information servers, map and point of interest (MPOI) servers, location servers, or the like.

An autonomous vehicle refers to a vehicle that may be configured to be in an autonomous mode in which the vehicle navigates through an environment with little or no input from the driver. Such autonomous vehicles may include a sensor system having one or more sensors configured to detect information related to the vehicle operating environment. The vehicle and its associated controller use the detected information to navigate through the environment. The autonomous vehicle 101 may operate in a manual mode, in a full-automatic driving mode, or in a partial-automatic driving mode.

In one embodiment, autonomous vehicle 101 includes, but is not limited to, a perception and planning system 110, a vehicle control system 111, a wireless communication system 112, a user interface system 113, and a sensor system 115. The autonomous vehicle 101 may also include certain common components included in common vehicles, such as: the components may be controlled by the vehicle control system 111 and/or the perception and planning system 110 using various communication signals and/or commands, such as acceleration signals or commands, deceleration signals or commands, steering signals or commands, braking signals or commands, and the like.

The components 110-115 may be communicatively coupled to each other via an interconnect, bus, network, or combination thereof. For example, the components 110-115 may be communicatively coupled to each other via a Controller Area Network (CAN) bus. The CAN bus is a vehicle bus standard designed to allow microcontrollers and devices to communicate with each other in applications without a host. It is a message-based protocol originally designed for multiplexing electrical wiring within automobiles, but is also used in many other environments.

Referring now to FIG. 2, in one embodiment, the sensor system 115 includes, but is not limited to, one or more cameras 211, a Global Positioning System (GPS) unit 212, an Inertial Measurement Unit (IMU) 213, a radar unit 214, a light detection and ranging (LIDAR) unit 215, and a vehicle-to-outside information exchange (V2X) unit 216. The GPS unit 212 may include a transceiver operable to provide information regarding the location of the autonomous vehicle. The IMU unit 213 may sense position and orientation changes of the autonomous vehicle based on inertial acceleration. Radar unit 214 may represent a system that utilizes radio signals to sense objects within the local environment of an autonomous vehicle. In some implementations, in addition to sensing an object, radar unit 214 may additionally sense a speed and/or a heading of the object. The LIDAR unit 215 may use a laser to sense objects in the environment of the autonomous vehicle. The LIDAR unit 215 may include, among other system components, one or more laser sources, a laser scanner, and one or more detectors. The camera 211 may include one or more devices for capturing images of the surroundings of the autonomous vehicle. The camera 211 may be a still camera and/or a video camera. The camera may be mechanically movable, for example, by mounting the camera on a rotating and/or tilting platform.

The sensor system 115 may also include other sensors such as: sonar sensors, infrared sensors, steering sensors, throttle sensors, brake sensors, and audio sensors (e.g., microphones). The audio sensor may be configured to collect sound from an environment surrounding the autonomous vehicle. The steering sensor may be configured to sense a steering angle of a steering wheel, wheels of a vehicle, or a combination thereof. The throttle sensor and the brake sensor sense a throttle position and a brake position of the vehicle, respectively. In some cases, the throttle sensor and the brake sensor may be integrated as an integrated throttle/brake sensor.

In one embodiment, the vehicle control system 111 includes, but is not limited to, a steering unit 201, a throttle unit 202 (also referred to as an acceleration unit), and a braking unit 203. The steering unit 201 is used to adjust the direction or forward direction of the vehicle. The throttle unit 202 is used to control the speed of the motor or engine, which in turn controls the speed and acceleration of the vehicle. The brake unit 203 decelerates the vehicle by providing friction to decelerate the wheels or tires of the vehicle. It should be noted that the components shown in fig. 2 may be implemented in hardware, software, or a combination thereof.

Referring back to fig. 1, the wireless communication system 112 allows communication between the autonomous vehicle 101 and external systems such as devices, sensors, other vehicles, and the like. For example, the wireless communication system 112 may communicate wirelessly with one or more devices directly or via a communication network, such as with servers 103-104 through network 102. The wireless communication system 112 may use any cellular communication network or Wireless Local Area Network (WLAN), for example, using WiFi, to communicate with another component or system. The wireless communication system 112 may communicate directly with devices (e.g., a passenger's mobile device, a display device, speakers within the vehicle 101), for example, using an infrared link, bluetooth, or the like. The user interface system 113 may be part of peripheral devices implemented within the vehicle 101, including, for example, a keyboard, a touch screen display device, a microphone, a speaker, and the like.

Some or all of the functions of the autonomous vehicle 101 may be controlled or managed by the awareness and planning system 110, particularly when operating in an autonomous mode. The perception and planning system 110 includes the necessary hardware (e.g., processors, memory, storage devices) and software (e.g., operating systems, planning and routing programs) to receive information from the sensor system 115, the control system 111, the wireless communication system 112, and/or the user interface system 113, process the received information, plan a route or path from a starting point to a destination point, and then drive the vehicle 101 based on the planning and control information. Alternatively, the perception and planning system 110 may be integrated with the vehicle control system 111.

For example, a user as a passenger may specify a starting location and destination of a trip, e.g., via a user interface. The perception and planning system 110 obtains trip related data. For example, the awareness and planning system 110 may obtain location and route information from an MPOI server, which may be part of the servers 103-104. The location server provides location services and the MPOI server provides map services and POIs for certain locations. Alternatively, such location and MPOI information may be cached locally in persistent storage of the awareness and planning system 110.

The perception and planning system 110 may also obtain real-time traffic information from a traffic information system or server (TIS) as the autonomous vehicle 101 moves along the route. It should be noted that servers 103-104 may be operated by third party entities. Alternatively, the functionality of servers 103-104 may be integrated with sensing and planning system 110. Based on the real-time traffic information, the MPOI information, and the location information, and the real-time local environment data (e.g., obstacles, objects, nearby vehicles) detected or sensed by the sensor system 115, the awareness and planning system 110 may plan an optimal route and drive the vehicle 101 according to the planned route, e.g., via the control system 111, to safely and efficiently reach the specified destination.

The server 103 may be a data analysis system to perform data analysis services for various clients. In one embodiment, the data analysis system 103 includes a data collector 121 and a machine learning engine 122. The data collector 121 collects driving statistics 123 from various vehicles (automated driving vehicles or conventional vehicles driven by human drivers). The driving statistics 123 include information indicating issued driving instructions (e.g., throttle, brake, steering instructions) and responses of the vehicle (e.g., speed, acceleration, deceleration, direction) captured by sensors of the vehicle at different points in time. The driving statistics 123 may also include information describing driving environments at different points in time, such as routes (including starting and destination locations), MPOI, road conditions, weather conditions, and the like.

Based on the driving statistics 123, the machine learning engine 122 generates or trains a set of rules, algorithms, and/or models 124 for various purposes. In one embodiment, for example, the algorithm/model 124 may include various interpolation models, such as an inverse distance weighted interpolation model. Inverse distance weighted interpolation is an interpolation technique that uses a weighted average of the properties from nearby sample points to estimate the size of the property at non-sampling locations. The inverse distance weighted interpolation model may be uploaded to the ADV for real-time use by the ADV.

Fig. 3A and 3B are block diagrams illustrating examples of a perception and planning system for use with an autonomous vehicle according to one embodiment. The system 300 may be implemented as part of the autonomous vehicle 101 of fig. 1, including but not limited to the perception and planning system 110, the control system 111, and the sensor system 115. Referring to fig. 3A-3B, the perception and planning system 110 includes, but is not limited to, a positioning module 301, a perception module 302, a prediction module 303, a decision module 304, a planning module 305, a control module 306, a routing/sampling module 307, and an auto-calibration module 308.

Some or all of the modules 301 to 308 may be implemented in software, hardware, or a combination thereof. For example, the modules may be installed in persistent storage 352, loaded into memory 351, and executed by one or more processors (not shown). It should be noted that some or all of these modules may be communicatively coupled to or integrated with some or all of the modules of the vehicle control system 111 of fig. 2. Some of the modules 301 to 308 may be integrated together as an integrated module. For example, the auto-calibration module 308 and the control module 306 may be integrated into a single module.

The positioning module 301 determines the current location of the autonomous vehicle 300 (e.g., using the GPS unit 212) and manages any data related to the user's journey or route. The positioning module 301 (also known as a map and route module) manages any data related to the user's journey or route. The user may log in and specify a starting location and destination of the trip, for example, via a user interface. The positioning module 301 communicates with other components of the autonomous vehicle 300, such as the map and route information 311, to obtain trip related data. For example, the positioning module 301 may obtain location and route information from a location server and a Map and POI (MPOI) server. The location server provides location services and the MPOI server provides map services and POIs for certain locations so that they can be cached as part of the map and route information 311. The positioning module 301 may also obtain real-time traffic information from a traffic information system or server as the autonomous vehicle 300 moves along a route.

Based on the sensor data provided by the sensor system 115 and the positioning information obtained by the positioning module 301, the perception module 302 determines the perception of the surrounding environment. The perception information may represent what an average driver would perceive around a vehicle that the driver is driving. Perception may include, for example, lane configurations in the form of objects (e.g., straight lanes or curved lanes), traffic light signals, relative positions of another vehicle, pedestrians, buildings, crosswalks, or other traffic-related signs (e.g., stop signs, let-off signs), etc. The lane configuration includes information describing one or more lanes, such as, for example, the shape of the lane (e.g., straight or curved), the width of the lane, the number of lanes in the road, one or two-way lanes, merging or splitting lanes, exit lanes, etc.

The perception module 302 may include a computer vision system or functionality of a computer vision system to process and analyze images captured by one or more cameras to identify objects and/or features in an autonomous vehicle environment. The objects may include traffic signals, road boundaries, other vehicles, pedestrians and/or obstacles, etc. Computer vision systems may use object recognition algorithms, video tracking, and other computer vision techniques. In some implementations, the computer vision system may map the environment, track the object, and estimate the speed of the object, among other things. The perception module 302 may also detect objects based on other sensor data provided by other sensors, such as radar and/or LIDAR.

For each object, the prediction module 303 predicts how the object will behave in this case. The prediction is performed based on perceived data that perceives the driving environment at a point in time that a set of map/route information 311 and traffic rules 312 are considered. For example, if the object is a vehicle in the opposite direction and the current driving environment includes an intersection, the prediction module 303 will predict whether the vehicle may move straight ahead or turn. If the sensed data indicates that the intersection is clear of traffic lights, the prediction module 303 may predict that the vehicle may need to be completely parked before entering the intersection. If the sensed data indicates that the vehicle is currently in a left-turn unique lane or a right-turn unique lane, the prediction module 303 may predict that the vehicle will be more likely to turn left or right, respectively.

For each object, decision module 304 makes a decision as to how to handle the object. For example, for a particular object (e.g., another vehicle in a cross-road) and metadata describing the object (e.g., speed, direction, turn angle), the decision module 304 decides how to meet the object (e.g., overtake, yield, stop, overrun). The decision module 304 may make such decisions according to a rule set, such as traffic rules or driving rules 312, which may be stored in persistent storage 352.

The routing module 307 is configured to provide one or more routes or paths from a starting point to a destination point. For a given journey from a starting location to a destination location, such as a given journey received from a user, the routing module 307 obtains the route and map information 311 and determines all possible routes or paths from the starting location to the destination location. The routing module 307 may generate reference lines in the form of a topographical map that determines each route from a starting location to a destination location. Reference lines refer to ideal routes or paths that are not subject to any interference by other vehicles, obstacles or traffic conditions, for example. That is, if there are no other vehicles, pedestrians, or obstacles on the road, the ADV should follow the reference line precisely or closely. The topography map is then provided to decision module 304 and/or planning module 305. The decision module 304 and/or the planning module 305 examine all of the possible routes to select and alter one of the best routes based on other data provided by other modules, such as traffic conditions from the positioning module 301, driving circumstances perceived by the perception module 302, and traffic conditions predicted by the prediction module 303. Depending on the particular driving environment at the point in time, the actual path or route used to control the ADV may be close to or different from the reference line provided by the routing module 307.

Based on the decisions for each of the perceived objects, the planning module 305 plans a path or route for the autonomous vehicle along with driving parameters (e.g., distance, speed, and/or turning angle). In other words, for a given object, decision module 304 decides what to do with the object, while planning module 305 determines how to do so. For example, for a given object, decision module 304 may decide to exceed the object, while planning module 305 may determine whether to exceed on the left or right of the object. Planning and control data is generated by the planning module 305, including information describing how the vehicle 300 will move in the next movement cycle (e.g., the next route/path segment). For example, the planning and control data may instruct the vehicle 300 to move 10 meters at a speed of 30 miles per hour (mph) and then to change to the right lane at a speed of 25 mph.

Based on the planning and control data, the control module 306 controls and drives the autonomous vehicle by sending appropriate commands or signals to the vehicle control system 111 according to the route or path defined by the planning and control data. The planning and control data includes sufficient information to drive the vehicle from a first point to a second point of the route or path at different points along the path or route using appropriate vehicle settings or driving parameters (e.g., throttle, brake, and turn commands).

In one embodiment, the programming phase is performed in a plurality of programming cycles (also referred to as command cycles), for example, in cycles of 100 milliseconds (ms) each. For each of the programming cycle or command cycle, one or more control commands will be issued based on the programming and control data. That is, for every 100ms, the planning module 305 plans the next route segment or path segment, including, for example, the target location and the time required for the ADV to reach the target location. Alternatively, the planning module 305 may also specify specific speeds, directions, and/or steering angles, etc. In one embodiment, the planning module 305 plans the route segment or path segment for a next predetermined period of time (such as 5 seconds). For each planning cycle, the planning module 305 plans the target location for the current cycle (e.g., the next 5 seconds) based on the target locations planned in the previous cycle. The control module 306 then generates one or more control commands (e.g., throttle, brake, steering control commands) based on the planning and control data for the current cycle.

It should be noted that the decision module 304 and the planning module 305 may be integrated as an integrated module. The decision module 304/planning module 305 may include a navigation system or functionality of a navigation system to determine a driving path of an autonomous vehicle. For example, the navigation system may determine a series of speeds and forward directions for achieving movement of the autonomous vehicle along the following paths: the path substantially avoids perceived obstructions while advancing the autonomous vehicle along a roadway-based path to a final destination. The destination may be set according to user input via the user interface system 113. The navigation system may dynamically update the driving path while the autonomous vehicle is running. The navigation system may combine data from the GPS system and one or more maps to determine a driving path for the autonomous vehicle.

The decision module 304/planning module 305 may also include a collision avoidance system or functions of a collision avoidance system to identify, evaluate, and avoid or otherwise clear potential obstacles in the environment of the autonomous vehicle. For example, the collision avoidance system may enable changes in the navigation of an autonomous vehicle by: one or more subsystems in the control system 111 are operated to take direction changing maneuvers, turn maneuvers, braking maneuvers, etc. The collision avoidance system may automatically determine a feasible obstacle avoidance maneuver based on surrounding traffic patterns, road conditions, and the like. The collision avoidance system may be configured such that no steer maneuvers are taken when other sensor systems detect vehicles, building obstacles, etc. located in the vicinity of where the autonomous vehicle will steer. The collision avoidance system may automatically select maneuvers that are both usable and maximize the safety of the occupants of the autonomous vehicle. The collision avoidance system may choose to predict an avoidance maneuver that causes a minimum amount of acceleration to occur in the passenger compartment of the autonomous vehicle.

FIG. 4 is a block diagram illustrating an example of an auto-calibration module according to one embodiment. Referring to FIG. 4, an auto-calibration module 308 is coupled to the sensor system 115 and the control module 306 of the vehicle to auto-calibrate the speed control calibration table/matrix 313 of the vehicle. The control module 306 may use the calibration table 313 to generate speed control commands in a subsequent planning cycle. The calibration table 313 may then be updated based on the feedback error of the speed control command, and then the command generation and calibration table update process may be repeated.

Referring to fig. 4, the auto-calibration module 308 may include an acceleration determiner module 401, a feedback error determiner and/or feedback error limiter module 403, and a calibration table updater module 405. The acceleration determiner module 401 may determine an acceleration of the ADV. The feedback error determiner/limiter module 403 may determine a feedback error (and/or limit the feedback error to a predetermined minimum/maximum value) for speed control of the ADV. The calibration table updater module 405 may update the calibration table based on the speed control feedback error.

FIG. 5 is an example of a calibration table according to one embodiment. Referring to fig. 5, the calibration table 313 may be a two-dimensional weighting curve shown in three dimensions. The table 313 may have two dimensions of x-y, the z dimension of which is a weighted value of the list, where x=speed/velocity (m/s), y=speed control command (%), and z=acceleration (m/s) ² ). Here, in one embodiment, the control command may be varied in a range between 100% and-100% for throttle (e.g., apply gasoline) and/or brake commands, and the acceleration may be between-10 and 10m/s ² And the speed may vary in the range of 0 to 30 m/s. The calibration table 313 can be used to look up y when x and z are known, or z when x and y are known, etc.

For example, given a speed (x) of 10.1m/s and a speed control command (y) of 1.8%, such a calibration table may be used to determine 0.5m/s ² Is used to determine the expected acceleration of the vehicle. Because the table 313 includes limited entries, i.e., non-contiguous, the calibration table can be read or updated using an interpolation model, such as an inverse distance weighted interpolation model. For example, the calibration table may include discrete speed values of 10m/s, 10.5m/s, and control commands of 1%, 2%, etc. In this case, a speed of 10.1m/s and a control command of 1.8% would not correspond to an entry, however, an entry corresponding to an adjacent entry for speeds of 10m/s and 10.5m/s and control commands of 1% and 2% may be used for interpolation. The calibration table may also be updated in a similar manner using the interpolation model.

FIG. 6 is a block diagram illustrating an exemplary feedback error limiter according to one embodiment. Referring to fig. 6, the feedback error limiter 600 may be part of the feedback error determiner/limiter 403 of fig. 4. A feedback error limiter may be used to limit the feedback response of the auto-calibration module. For example, calibration table updates and command generation may be performed continuously for each planning cycle. However, the minimal feedback error should avoid triggering a calibration table update, as it may not be necessary and only wastes computational resources. In addition, the maximum feedback error may trigger the update of the calibration table to the maximum feedback error value, thereby avoiding any instability. In one embodiment, the feedback limiter 600 may be configured to output a feedback error whenever the feedback value is greater than a predetermined minimum value. For example, for feedback values less than a predetermined minimum value, no feedback error is provided and the calibration table is not updated. In another embodiment, the feedback limiter may be configured to output a feedback error that is up to a predetermined maximum. In this case, the calibration table will be updated according to the maximum limit for any feedback error greater than the maximum value. Examples of minimum/maximum values may be 0.1 and 1.0, respectively.

Fig. 7 is a block diagram illustrating an example of a portion of a calibration table according to one embodiment. Referring to fig. 7, the calibration table portion 700 may be any number of list values from the calibration table 313 of fig. 5. For example, table portion 700 may include four entry values 701-704 and a point of interest 705. Items 701 to 704 may be a distance d1, a distance d2, a distance d3, a distance d4, respectively, away from the point 705. For example, for x-y entry values, values 701 through 704 may correspond to (x, y) = { (10.0 m/s, 2%), (10.5 m/s, 2%), (10.0 m/s, 1%), (10.5 m/s, 1%) }, and point 705 may correspond to (x, y) = (10.1,1.8%). Here, the value of point 705 may be determined from table portion 700 using the following formula:

where z is the value of the point of interest, zi is the value of the entry around the point of interest, and di is the distance of the respective surrounding entries.

In one embodiment, the value of point 705 or val (705) is equal to (val (701)/d1+val (702)/d2+val (703)/d3+val (704)/d4)/(1/d1+1/d2+1/d3+1/d 4). In another embodiment, to update the calibration table portion 700, the values at points 705 may update the entry values in the table portion 700 by an inverse distance weighted interpolation model using the following weighting formula:

Where weight will apply to the table entry corresponding to dj and di is the distance of the respective surrounding entries of the point of interest.

Fig. 8A-8D are block diagrams illustrating examples of automatic calibration updates according to one embodiment. Referring to fig. 8A to 8D, in one embodiment, fig. 8A to 8B may correspond to front and rear images of a calibration table updated according to a first planning period. Fig. 8C through 8D may correspond to front and rear images of the calibration table updated according to the second planning period. As shown, referring to fig. 8A, four entries are updated for the surrounding point entries in fig. 8B, and referring to fig. 8C, the entries in fig. 8D are updated. Here, different shading of entries represents table entry values of different sizes.

Thus, the process performed by the auto-calibration module 308 may be summarized in the following example. Referring to fig. 4-6, in one embodiment, during a first planning period, the auto-calibration module 308 may obtain rate/speed measurements 411 from the IMU units of the ADV 101 via the sensor system 115, and may obtain control commands and status 413 (e.g., success/failure) via the control module 306. Based on the speed measurements and control commands and their status, the acceleration determiner module 401 uses the calibration table 313 to determine the expected acceleration. As described above, for a limited calibration table, some interpolation algorithms or models may be used to determine the expected acceleration for points of interest that are not on the table entry.

For a second (subsequent) planning period, e.g., 200ms later, when the acceleration is valid, the auto-calibration module 308 obtains an acceleration measurement via the IMU unit and compares the acceleration measurement to an expected acceleration to calculate a delta acceleration (e.g., feedback error). Based on the calculated delta acceleration or feedback error, the feedback error limiter 403 determines the actual feedback error to be applied to the calibration table 313. The calibration table updater module 405 then updates the calibration table using an interpolation algorithm. ADV 101 may then use the calibration table to generate subsequent speed control commands based on the planned speed of the ADV for the corresponding planning period. Then, the calibration table update and command generation process is repeated.

FIG. 9 is a flow chart illustrating a method according to one embodiment. Process 900 may be performed by processing logic that may comprise software, hardware, or a combination thereof. For example, process 900 may be performed by auto-calibration module 308 of fig. 3A. Referring to fig. 9, at block 901 processing logic receives a first control command and a speed measurement of an ADV. At block 902, processing logic determines an expected acceleration of the ADV based on the speed measurement and the first control command. At block 903, processing logic receives an acceleration measurement of the ADV. At block 904, processing logic determines a feedback error based on the acceleration measurement and the expected acceleration. At block 905, processing logic updates a portion of the calibration table based on the determined feedback error. At block 906, processing logic generates a second control command to control the ADV based on the calibration table with the updated portion to autonomously control the ADV in accordance with the second control command.

In one embodiment, the speed and acceleration measurements are performed by Inertial Measurement Unit (IMU) sensors of the ADV. In one embodiment, a calibration table is used to determine the expected acceleration based on the speed measurement and the first control command.

In one embodiment, updating the portion of the calibration table further comprises determining an update point located on the calibration table based on the first control command and the speed measurement. In one embodiment, the calibration table is a three-dimensional table having table entries for control commands, speed, and acceleration dimensions.

In one embodiment, the control command includes: acceleration or braking commands, and has a range of 100% to-100%. In another embodiment, the table entries are discrete entries. In another embodiment, the table entries are updated according to a spatial interpolation model. In another embodiment, the spatial interpolation model comprises an inverse distance weighted interpolation model. In another embodiment, a table entry with a distance d1 from the update point is updated based on a weighting factor (1/d 1)/(1/d1+1/d2+1/d3+1/d 4), where d1, d2, d3 and d4 are the distances of four surrounding entries for a given acceleration, respectively.

FIG. 10 is a flow chart illustrating a method according to one embodiment. Process 1000 may be performed by processing logic that may comprise software, hardware, or a combination thereof. For example, process 1000 may be performed by auto-calibration module 308 of fig. 3A. Referring to fig. 10, at block 1001 processing logic receives a current speed of an ADV. At block 1002, processing logic calculates acceleration based on the planned speed and the current speed of the ADV. At block 1003, processing logic determines a second control command using the calibration table based on the calculated acceleration and the current speed of the received ADV. At block 1004, processing logic generates a second control command.

It should be noted that some or all of the components shown and described above may be implemented in software, hardware, or a combination thereof. For example, such components may be implemented as software installed and stored in persistent storage, which may be loaded into memory by a processor (not shown) and executed in memory to implement the processes or operations described throughout this application. Alternatively, such components may be implemented as executable code programmed into or embedded in dedicated hardware, such as an integrated circuit (e.g., an application specific integrated circuit or ASIC), a Digital Signal Processor (DSP), or a Field Programmable Gate Array (FPGA), which is accessible via a respective driver and/or operating system from an application. Further, such components may be implemented as specific hardware logic in a processor or processor core as part of an instruction set accessible by a software component through one or more specific instructions.

FIG. 11 is a block diagram illustrating an example of a data processing system that may be used with one embodiment of the present disclosure. For example, system 1500 may represent any of the data processing systems described above that perform any of the processes or methods described above, such as, for example, perception and planning system 110 or servers 103-104 of FIG. 1. The system 1500 may include many different components. These components may be implemented as Integrated Circuits (ICs), portions of integrated circuits, discrete electronic devices, or other modules adapted for use with a circuit board, such as a motherboard or add-in card, of a computer system, or as components that are otherwise incorporated within a chassis of a computer system.

It should also be noted that system 1500 is intended to illustrate high-level views of many of the components of a computer system. However, it is to be understood that additional components may be present in some embodiments, and further, that different arrangements of the components shown may be present in other embodiments. System 1500 may represent a desktop computer, laptop computer, tablet computer, server, mobile phone, media player, personal Digital Assistant (PDA), smart watch, personal communicator, gaming device, network router or hub, wireless Access Point (AP) or repeater, set top box, or a combination thereof. Furthermore, while only a single machine or system is illustrated, the term "machine" or "system" shall also be taken to include any collection of machines or systems that individually or jointly execute a set (or multiple sets) of instructions to perform any one or more of the methodologies discussed herein.

In one embodiment, system 1500 includes a processor 1501, memory 1503, and devices 1505 through 1508 connected by a bus or interconnect 1510. Processor 1501 may represent a single processor or multiple processors including a single processor core or multiple processor cores. The processor 1501 may represent one or more general-purpose processors, such as a microprocessor, central Processing Unit (CPU), or the like. More specifically, the processor 1501 may be a Complex Instruction Set Computing (CISC) microprocessor, a Reduced Instruction Set Computing (RISC) microprocessor, a Very Long Instruction Word (VLIW) microprocessor, or a processor implementing other instruction sets, or a processor implementing a combination of instruction sets. The processor 1501 may also be one or more special purpose processors such as an Application Specific Integrated Circuit (ASIC), a cellular or baseband processor, a Field Programmable Gate Array (FPGA), a Digital Signal Processor (DSP), a network processor, a graphics processor, a communications processor, an encryption processor, a co-processor, an embedded processor, or any other type of logic capable of processing instructions.

The processor 1501 (which may be a low power multi-core processor socket, such as an ultra-low voltage processor) may act as the main processing unit and central hub for communication with the various components of the system. Such a processor may be implemented as a system on a chip (SoC). The processor 1501 is configured to execute instructions for performing the operations and steps discussed herein. The system 1500 may also include a graphics interface in communication with an optional graphics subsystem 1504, which graphics subsystem 1504 may include a display controller, a graphics processor, and/or a display device.

The processor 1501 may be in communication with a memory 1503, which memory 1503 may be implemented via multiple memory devices in one embodiment to provide for quantitative system storage. Memory 1503 may include one or more volatile storage (or memory) devices such as Random Access Memory (RAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), static RAM (SRAM), or other types of storage devices. The memory 1503 may store information including sequences of instructions that are executed by the processor 1501, or any other device. For example, executable code and/or data for various operating systems, device drivers, firmware (e.g., input output basic system or BIOS), and/or applications may be loaded into memory 1503 and executed by processor 1501. The operating system may be any type of operating system, e.g., a Robotic Operating System (ROS), from among others The companyOperating System, mac ∈Di from apple Inc.)>From->The companyLINUX, UNIX, or other real-time or embedded operating systems.

System 1500 may also include IO devices, such as devices 1505 through 1508, including network interface device 1505, optional input device 1506, and other optional IO devices 1507. Network interface device 1505 may include a wireless transceiver and/or a Network Interface Card (NIC). The wireless transceiver may be a WiFi transceiver, an infrared transceiver, a bluetooth transceiver, a WiMax transceiver, a wireless cellular telephone transceiver, a satellite transceiver (e.g., a Global Positioning System (GPS) transceiver), or other Radio Frequency (RF) transceiver, or a combination thereof. The NIC may be an ethernet card.

The input device 1506 may include a mouse, a touchpad, a touch-sensitive screen (which may be integrated with the display device 1504), a pointing device (such as a stylus), and/or a keyboard (e.g., a physical keyboard or a virtual keyboard displayed as part of the touch-sensitive screen). For example, the input device 1506 may include a touch screen controller coupled to a touch screen. Touch screens and touch screen controllers, for example, may use any of a variety of touch sensitive technologies including, but not limited to, capacitive, resistive, infrared, and surface acoustic wave technologies, as well as other proximity sensor arrays or other elements for determining one or more points of contact with the touch screen to detect contact and movement or discontinuity thereof.

IO device 1507 may include audio devices. The audio device may include a speaker and/or microphone to facilitate voice-enabled functions, such as voice recognition, voice replication, digital recording, and/or telephony functions. Other IO devices 1507 may also include Universal Serial Bus (USB) ports, parallel ports, serial ports, printers, network interfaces, bus bridges (e.g., PCI-PCI bridges), sensors (e.g., such as accelerometer motion sensors, gyroscopes, magnetometers, light sensors, compasses, proximity sensors, etc.), or combinations thereof. The device 1507 may also include an imaging processing subsystem (e.g., a camera) that may include an optical sensor, such as a charge-coupled device (CCD) or a Complementary Metal Oxide Semiconductor (CMOS) optical sensor, for facilitating camera functions, such as recording photographs and video clips. Some sensors may be coupled to the interconnect 1510 via a sensor hub (not shown), while other devices, such as a keyboard or thermal sensor, may be controlled by an embedded controller (not shown) depending on the specific configuration or design of the system 1500.

To provide persistent storage of information, such as data, applications, one or more operating systems, etc., a mass storage device (not shown) may also be coupled to the processor 1501. In various embodiments, such mass storage devices may be implemented via Solid State Devices (SSDs) in order to achieve thinner and lighter system designs and improve system responsiveness. However, in other implementations, the mass storage device may be implemented primarily using a Hard Disk Drive (HDD), with a smaller amount of SSD storage acting as an SSD cache to enable non-volatile storage of context state and other such information during power-down events, enabling fast power-up upon system activity restart. In addition, the flash memory device may be coupled to the processor 1501, for example, via a Serial Peripheral Interface (SPI). Such flash memory devices may provide non-volatile storage of system software, including the BIOS of the system, as well as other firmware.

The storage 1508 may include a computer-accessible storage medium 1509 (also referred to as a machine-readable storage medium or computer-readable medium) having stored thereon one or more sets of instructions or software (e.g., modules, units, and/or logic 1528) embodying any one or more of the methodologies or functions described herein. The processing module/unit/logic 1528 may represent any of the components described above, such as, for example, the auto-calibration module 308 of fig. 3A. The processing module/unit/logic 1528 may also reside, completely or at least partially, within the memory 1503 and/or within the processor 1501 during execution thereof by the data processing system 1500, the memory 1503 and the processor 1501, the data processing system 1500, the memory 1503 and the processor 1501 also constituting machine-accessible storage media. The processing module/unit/logic 1528 can also transmit or receive over a network via the network interface device 1505.

The computer readable storage medium 1509 may also be used to permanently store some of the software functions described above. While computer-readable storage medium 1509 is shown in an exemplary embodiment to be a single medium, the term "computer-readable storage medium" should be taken to include a single medium or multiple media (e.g., a centralized or distributed database, and/or associated caches and servers) that store the one or more sets of instructions. The term "computer-readable storage medium" shall also be taken to include any medium that is capable of storing or encoding a set of instructions for execution by the machine and that cause the machine to perform any one or more of the methodologies of the present disclosure. Accordingly, the term "computer-readable storage medium" shall be taken to include, but not be limited to, solid-state memories, as well as optical and magnetic media, or any other non-transitory machine-readable medium.

The processing modules/units/logic 1528, components, and other features described herein may be implemented as discrete hardware components or integrated in the functionality of hardware components (such as ASICS, FPGA, DSP or similar devices). Furthermore, the processing module/unit/logic 1528 may be implemented as firmware or functional circuitry within a hardware device. Furthermore, the processing module/unit/logic 1528 may be implemented in any combination of hardware devices and software components.

It should be noted that while system 1500 is illustrated as having various components of a data processing system, it is not intended to represent any particular architecture or manner of interconnecting the components; as such details are not germane to the embodiments of the present disclosure. It will also be appreciated that network computers, hand held computers, mobile phones, servers and/or other data processing systems which have fewer components or possibly more components may also be used with embodiments of the present disclosure.

Some portions of the preceding detailed description have been presented in terms of algorithms and symbolic representations of operations on data bits within a computer memory. These algorithmic descriptions and representations are the means used by those skilled in the data processing arts to most effectively convey the substance of their work to others skilled in the art. An algorithm is here, and generally, considered to be a self-consistent sequence of operations leading to a desired result. The operations are those requiring physical manipulations of physical quantities.

It should be borne in mind, however, that all of these and similar terms are to be associated with the appropriate physical quantities and are merely convenient labels applied to these quantities. Unless specifically stated otherwise as apparent from the above discussion, it is appreciated that throughout the description, discussions utilizing terms such as those set forth in the appended claims, refer to the action and processes of a computer system, or similar electronic computing device, that manipulates and transforms data represented as physical (electronic) quantities within the computer system's registers and memories into other data similarly represented as physical quantities within the computer system memories or registers or other such information storage, transmission or display devices.

Embodiments of the present disclosure also relate to an apparatus for performing the operations herein. Such a computer program is stored in a non-transitory computer readable medium. A machine-readable medium includes any mechanism for storing information in a form readable by a machine (e.g., a computer). For example, a machine-readable (e.g., computer-readable) medium includes a machine (e.g., a computer) readable storage medium (e.g., read only memory ("ROM"), random access memory ("RAM"), magnetic disk storage medium, optical storage medium, flash memory device).

The processes or methods depicted in the preceding figures may be performed by processing logic that comprises hardware (e.g., circuitry, dedicated logic, etc.), software (e.g., embodied on a non-transitory computer readable medium), or a combination of both. Although the process or method is described above in terms of some sequential operations, it should be appreciated that some of the operations may be performed in a different order. Further, some operations may be performed in parallel rather than sequentially.

Embodiments of the present disclosure are not described with reference to any particular programming language. It will be appreciated that a variety of programming languages may be used to implement the teachings of embodiments of the disclosure as described herein.

In the foregoing specification, embodiments of the present disclosure have been described with reference to specific exemplary embodiments thereof. It will be evident that various modifications may be made thereto without departing from the broader spirit and scope of the disclosure as set forth in the following claims. The specification and drawings are, accordingly, to be regarded in an illustrative rather than a restrictive sense.

Claims

1. A computer-implemented method of controlling an autonomous vehicle, the method comprising:

Receiving a first control command and a speed measurement of the autonomous vehicle;

determining an expected acceleration of the autonomous vehicle based on the speed measurement and the first control command;

receiving an acceleration measurement of the autonomous vehicle;

determining a feedback error based on the acceleration measurement and the expected acceleration;

updating a portion of a calibration table based on the determined feedback error, the calibration table being a two-dimensional curve having table entries, wherein the table entries have coordinates of control commands and speeds, and the entry values are acceleration values of the respective coordinates; and

a second control command to control the autonomous vehicle is generated based on the calibration table with the updated portion to autonomously control the autonomous vehicle in accordance with the second control command.

2. The method of claim 1, wherein the speed measurement and the acceleration measurement are performed by an inertial measurement unit sensor of the autonomous vehicle.

3. The method of claim 1, wherein the expected acceleration is determined using the calibration table based on the speed measurement and the first control command.

4. The method of claim 1, wherein updating the portion of the calibration table further comprises determining an update point located on the calibration table based on the first control command and the speed measurement.

5. The method of claim 4, wherein the table entry is a discrete entry.

6. The method of claim 5, wherein the table entries are updated according to a spatial interpolation model.

7. The method of claim 6, wherein the spatial interpolation model comprises an inverse distance weighted interpolation model.

8. The method of claim 7, wherein table entries at a distance d1 from the update point are updated based on a weighting factor (1/d1)/(1/d1+1/d2+1/d3+1/d 4), wherein d1, d2, d3 and d4 are the distances of four surrounding entries for a given acceleration, respectively.

9. The method of claim 1, wherein determining a feedback error based on the acceleration measurement and the expected acceleration includes limiting the feedback error to a predetermined minimum/maximum value.

10. The method of claim 1, wherein generating a second control command comprises:

receiving a current speed of the autonomous vehicle;

calculating an acceleration based on the planned speed and the current speed of the autonomous vehicle;

determining the second control command using the calibration table based on the calculated acceleration and the received current speed of the autonomous vehicle; and

And generating the second control command.

11. A non-transitory machine-readable medium storing instructions that, when executed by a processor, cause the processor to perform operations comprising:

receiving a first control command and a speed measurement of an autonomous vehicle;

receiving an acceleration measurement of the autonomous vehicle;

12. The non-transitory machine readable medium of claim 11, wherein the speed measurement and the acceleration measurement are performed by an inertial measurement unit sensor of the autonomous vehicle.

13. The non-transitory machine readable medium of claim 11, wherein the expected acceleration is determined using the calibration table based on the speed measurement and the first control command.

14. The non-transitory machine-readable medium of claim 11, wherein updating the portion of the calibration table further comprises determining an update point located on the calibration table based on the first control command and the speed measurement.

15. A data processing system, comprising:

a processor; and

a memory coupled to the processor to store instructions that, when executed by the processor, cause the processor to perform operations comprising:

receiving an acceleration measurement of the autonomous vehicle;

16. The system of claim 15, wherein the speed measurement and the acceleration measurement are performed by an inertial measurement unit sensor of the autonomous vehicle.

17. The system of claim 15, wherein the expected acceleration is determined using the calibration table based on the speed measurement and the first control command.

18. The system of claim 15, wherein updating the portion of the calibration table further comprises determining an update point located on the calibration table based on the first control command and the speed measurement.